spatial data
types of spatial data
- “space” is generally 2D (\(\mathbb{R}^2\); could be surface of a sphere)
- also: networks, trees, lattices, …
- features: points, polygons, lines (and collections thereof); rasters
- also: continuous or categorical values associated with features
- counts (disease incidence) or continuous values (GDP) associated with polygons (countries/provinces/counties)
- values associated with grid cells (digital elevation models)
- points (locations of murders)
transformations/summarization
e.g.Â
- points to density fields (2D kernel density estimation)
- points to polygon values (square or hex binning)
- fields to polygons (contouring)
- point values to fields (interpolation;
akima does bicubic/bilinear)
typical plots
- points on maps
- contour plots on maps
- choropleth maps (filled polygons)
spatial graph gallery
spatial data challenges
spatial data and colour
- colour issues are much more salient for spatial data
- big blocks of colour
- often use colour gradients for continuous data
- continuous vs segmented, appropriate endpoints (background)
- ColorBrewer project,
RColorBrewer package
primary packages for spatial data manipulation
sf (“simple features”): tidy spatial data (web page)
maptools
spatial plotting challenges
- top of Cleveland hierarchy (x,y coordinates) are used up
- insets (Alaska/Hawaii etc.)
- map decoration
- representing uncertainties: @correll_value-suppressing_2018, @maceachren_visualizing_2005, @koo_geovisualizing_2018
- not misrepresenting areas (e.g. cartograms: @perrier_topogram_2019, @hohle_cartograms_2016)
- linking?
primary R packages
maps (base-R maps, some basic spatial data sets)
ggmap (maps in ggplot, including downloading data from google maps etc.)
leaflet (map widget)
tmap (an alternative ggplot-like approach: see here)
to do